Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction
Contributo in Atti di convegno
Data di Pubblicazione:
2015
Citazione:
Estimation of a Piecewise Exponential Model by Bayesian P-splines Techniques for Prognostic Assessment and Prediction / G. Marano, P. Boracchi, E.M. Biganzoli (LECTURE NOTES IN ARTIFICIAL INTELLIGENCE). - In: Computational Intelligence Methods for Bioinformatics and Biostatistics / [a cura di] C. Di Serio, P. Liò, A. Nonis, R. Tagliaferri. - [s.l] : Springer Verlag, 2015 Jun. - ISBN 9783319244617. - pp. 183-198 (( Intervento presentato al 11. convegno International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB) tenutosi a Cambridge nel 2014 [10.1007/978-3-319-24462-4_16].
Abstract:
Methods for fitting survival regression models with a penalized smoothed hazard function have been recently discussed, even though they could be cumbersome. A simpler alternative which does not require specific software packages could be fitting a penalized piecewise exponential model. In this work the implementation of such strategy in Win-BUGS is illustrated, and preliminary results are reported concerning the application of Bayesian P-splines techniques. The technique is applied to a pre-specified model in which the number and positions of knots were fixed on the basis of clinical knowledge, thus defining a non-standard smoothing problem.
Tipologia IRIS:
03 - Contributo in volume
Keywords:
Survival analysis; Hazard Smoothing; Bayesian P-splines; Piecewise Exponential Model
Elenco autori:
G. Marano, P. Boracchi, E.M. Biganzoli
Link alla scheda completa:
Titolo del libro:
Computational Intelligence Methods for Bioinformatics and Biostatistics